Near-optimal solution alternatives for the synthesis of distributed energy supply systems

Abstract

An automated decision support framework is presented for the optimization-based synthesis of distributed energy supply systems (DESS). The framework supports decision makers through the generation and analysis of a set of promising candidate solutions rather than a single optimal solution only. For this purpose, an automated three-step synthesis procedure is proposed that first determines the solution optimizing a single criterion; second, generates near-optimal solution alternatives, and third, computes Pareto-optimal compromise solutions. The solution set is evaluated a posteriori to generate deeper understanding of the synthesis problem. In this paper, a real-world problem from the pharmaceutical industry is considered. For economic optimization, first, the net present value is maximized. Secondly, structurally different, near-optimal solution alternatives are identified employing an integer-cut based solution procedure. Finally, the ε- constraint method is used to generate a Pareto-front representing the trade-offs between investment cost and cumulative energy demand. It is shown that the three-step procedure yields valuable insight into the synthesis problem enabling rational and far-sighted design decisions.